Introducing a New Approach to Improve Recommendation Systems for Online Marketplaces

In large-scale online marketplaces, recommendation systems play a crucial role in helping users discover new content. However, current systems for item-to-item recommendations often lack a deep understanding of context, which can be problematic when navigating nuanced item relationships. Some item pairs may have confusing or controversial connections, leading to negative user experiences and brand perception. For instance, recommending a book about one sports team to someone reading a book about its biggest rival can be a disappointing experience, despite the surface-level similarities of the books. To address this issue, we propose a classifier that identifies and prevents such problematic recommendations, ultimately enhancing user experiences overall.

The Importance of Contextually Relevant Recommendations

Contextually relevant recommendations are vital in improving user satisfaction and driving engagement. By better understanding the relationships between items, recommendation systems can provide users with more meaningful and tailored suggestions. However, current systems often struggle with identifying and filtering out problematic recommendations, leading to subpar user experiences.

Introducing our Solution

Our proposed approach tackles the challenge of refining recommendation systems by utilizing active learning and involving human raters for data labeling. By implementing active learning, we effectively sample difficult examples from sensitive item categories. This enables us to gather valuable data and insights from human raters, ensuring accurate and reliable recommendations. We believe that by combining machine learning techniques with human intelligence, our approach can significantly enhance the quality of recommendations and prevent problematic item-to-item matches.

Testing the Efficacy of our System

We conducted offline experiments to evaluate the effectiveness of our system in identifying and filtering problematic recommendations while still maintaining recommendation quality. Our results demonstrate that our approach successfully identifies and addresses problematic item-to-item matches, thereby improving user experiences and mitigating potential negative impacts on brand perception.

*Please note that the paper referenced in this article has multiple authors who contributed equally.